SCOVIS - Self Configurable Cognitive Video Supervision

SCOVIS investigated weakly supervised learning algorithms and self-adaptation strategies for analysis of visually observable operations in an industrial context environment. SCOVIS research directly affects ease of deployment and minimises effort of operation of monitoring systems and is unique in the sense that it links object learning using low-level object descriptors and procedure learning with adaptation mechanisms and active camera network coordination. SCOVIS advocates a synergistic approach that combines largely unsupervised learning and model evolution in a bootstrapping process; it involves continuous learning from visual content in order to enrich the models and, inversely, the direct use of these models to enhance the extraction. In the SCOVIS application scenario user interaction will be significantly reduced compared to current methods. The system will be able to calculate the camera spatial relations automatically (self-configuration) for coupled, uncoupled and active cameras. The user will define a set of objects and procedures of interest during a very short supervised learning phase, while the associations with low-level descriptors will be automatically learnt. The resulting models will be significantly enhanced through online data acquisition and unsupervised learning (adaptation). The enhanced models will be able to be verified and potentially adapted through relevance feedback. The main measurable objective of SCOVIS will be to significantly improve the versatility and the performance of current monitoring systems. The resulting technology will enable the easy installation of intelligent supervision systems, which has not been possible so far, due to the prohibitively high manual effort and the inability to model complex visual processes. The produced technology will be evaluated through realistic scenarios related to industry and public infrastructure. The proposed research will be performed with absolute respect to privacy and personal data of monitored individuals.